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predict.py
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predict.py
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from keras.models import load_model
import cv2
from PIL import Image
import matplotlib.pyplot as plt
import numpy as np
import random
from matplotlib.image import imread
import pandas as pd
data_dir = "dataset"
CategoriesNames = { 0:'Speed limit (20km/h)',
1:'Speed limit (30km/h)',
2:'Speed limit (50km/h)',
3:'Speed limit (60km/h)',
4:'Speed limit (70km/h)',
5:'Speed limit (80km/h)',
6:'End of speed limit (80km/h)',
7:'Speed limit (100km/h)',
8:'Speed limit (120km/h)',
9:'No passing',
10:'No passing veh over 3.5 tons',
11:'Right-of-way at intersection',
12:'Priority road',
13:'Yield',
14:'Stop',
15:'No vehicles',
16:'Veh > 3.5 tons prohibited',
17:'No entry',
18:'General caution',
19:'Dangerous curve left',
20:'Dangerous curve right',
21:'Double curve',
22:'Bumpy road',
23:'Slippery road',
24:'Road narrows on the right',
25:'Road work',
26:'Traffic signals',
27:'Pedestrians',
28:'Children crossing',
29:'Bicycles crossing',
30:'Beware of ice/snow',
31:'Wild animals crossing',
32:'End speed + passing limits',
33:'Turn right ahead',
34:'Turn left ahead',
35:'Ahead only',
36:'Go straight or right',
37:'Go straight or left',
38:'Keep right',
39:'Keep left',
40:'Roundabout mandatory',
41:'End of no passing',
42:'End no passing veh > 3.5 tons' }
model_path = "ouput_model_training/secondModel/model.h5"
loaded_model = load_model(model_path)
"""Prediction on single image """
# img = cv2.imread("dataset/Test/00001.png")
#
# image_fromarray = Image.fromarray(img, 'RGB')
# resize_image = image_fromarray.resize((30, 30))
#
# expand_input = np.expand_dims(resize_image,axis=0)
# input_data = np.array(expand_input)
# input_data = input_data/255
#
# pred = loaded_model.predict(input_data)
# result = pred.argmax()
# print(CategoriesNames[result])
"""predicting 5 random images from test data"""
test = pd.read_csv(data_dir + '/Test.csv')
imgs = test["Path"].values
plt.figure(figsize=(25,25))
for i in range(1,6):
plt.subplot(1,5,i)
random_img_path = data_dir + '/' + random.choice(imgs)
rand_img = imread(random_img_path)
image_fromarray = Image.fromarray(rand_img, 'RGB')
resize_image=image_fromarray.resize((30, 30))
expand_input=np.expand_dims(resize_image, axis=0)
input_data=np.array(expand_input)
input_data=input_data / 255
pred=loaded_model.predict(input_data)
result=pred.argmax()
plt.imshow(rand_img)
plt.xlabel(CategoriesNames[result], color="r")
plt.show()